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| Section | Weight | Objectives |
|---|---|---|
| Governance, Evaluation & Trust | 15% | - Observability and reliability - Agent performance evaluation - Guardrails and responsible AI |
| Context & Knowledge Management | 20% | - Knowledge base integration - Context packages and grounding - Data sources and retrieval methods |
| Orchestration & Human-in-the-Loop | 20% | - Escalations and Action Center - UiPath Maestro and BPMN modeling - Service tasks and agent invocation |
| Agentic Automation Fundamentals | 20% | - Agents vs traditional robots - Core principles of agentic automation - AI, LLM and generative AI concepts |
| Agent Design & Development | 25% | - Prompt engineering best practices - Agent Builder in Studio Web - Tools, connections and integration services |
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33. Frage
Which similarity search function is leveraged when Context Grounding is used by UiPath Products like Agents?
Antwort: A
Begründung:
Cis correct - UiPath'sContext Groundingsystem usescosine similarity searchto retrieve the most relevant documents, passages, or semantic chunks from indexed data during runtime.
This retrieval process is critical for:
* Minimizing hallucination
* Enhancingprompt grounding
* Connecting agents toreal-time enterprise knowledge
Here's how it works:
* All documents areembedded into vector spaceusing a model (e.g., OpenAI or Azure OpenAI embeddings)
* At runtime, the agent's query isconverted into a vector
* Acosine similaritycomparison is used to fetch themost semantically similar documentsbased onangle (not magnitude)between vectors This enables agents to provideaccurate, contextual answers, like referencing HR policies or IT guides.
Other options:
* A (Softmax) is a classifier, not a search method
* B (Sigmoid) is for binary classification
* D (ReLU) is an activation function in neural nets - not for search
34. Frage
When adding an index for querying data stored in CSV files, what advanced feature does UiPath Context Grounding provide to optimize retrieval?
Antwort: D
Begründung:
Dis correct - UiPathContext Groundingsupports querying unstructured and semi-structured data, including CSV files, by embedding their content intosemantic representationssuch asJSON-formatted chunksduring indexing.
Here's how this works for CSVs:
* UiPathparses the tabular dataand maps each row or section into asemantically rich format(e.g., JSON)
* These JSON-structured embeddings are then stored inECS Indexes(Enterprise Context Store)
* When an LLM agent queries the index, it retrieves themost contextually relevant data, even across large datasets This unlocks:
* Smarter question answeringfrom tabular data
* Cross-referencing multiple fieldsin a single query
* Enhanced LLM understanding by transforming flat rows intorelational, structured prompts Option A is misleading - LLMs rely onsemantic similarity, not SQL-like structured queries.
B is false - CSV is not auto-converted into XLSX.
C is incorrect - streaming is not yet supported; indexing is a prerequisite.
In short, UiPath enablessemantic grounding of structured datalike CSVs by reformatting them intoJSON- style embeddings, improving retrieval quality, summarization, and task-specific use cases.
35. Frage
What is a System Prompt?
Antwort: B
Begründung:
Cis the correct answer - in UiPath's Agentic Automation framework, theSystem Promptis acrucial configuration elementthat defines theagent's identity, objectives, behavioral rules, and tool usage logic.
It typically includes:
* Agent Role: e.g., "You are a procurement assistant"
* Goals: "Classify, summarize, or validate supplier quotes"
* Constraints: e.g., "Don't exceed 100 words", "Only use escalation when criteria X is met"
* Tool Usage: "Use Slack tool to notify team if X occurs"
* Escalation Logic: "Escalate to human if confidence is below threshold"
* Context Integration: "Use grounded context from ECS Index when available" This helps the LLM behaveconsistentlyandtransparently, even in unpredictable or complex workflows. It also acts as thestarting configurationfor the agent - informing every decision it makes during runtime.
Option A is incorrect - System Prompts are written innatural language, not code.
B is false - they allow fordynamic adaptation, especially when used with memory and tools.
D is incomplete - the system promptdoes covergoals, constraints, and sequencing of steps.
Bottom line: theSystem Prompt is the "brain" behind the agent, telling it what to do, how to do it, when to act, and when to escalate - all in anatural language-driven, declarative format.
36. Frage
Which of the following best describes a challenge faced by traditional automation in complex business processes?
Antwort: B
Begründung:
The correct answer isC, which highlights one of the core limitations of traditional rule-based automation (RPA) - itsinability to handle unstructured tasks that require human-like reasoning and contextual awareness.
According to UiPath's Agentic Automation documentation, traditional automation excels atrepetitive, rules- based, structuredtasks. However, it struggles when:
* Input data isunstructured(like emails, PDFs, or chat logs)
* Tasks requirecontextual understanding, decision-making, or judgment
* Processes span across systems with unpredictable flows (e.g., CRM + ERP + email) This is exactly whereAgentic Automationsteps in. It augments classic automation by embeddingLLMs, AI agents, and decision intelligenceto manage tasks involving ambiguity, variability, and natural language - things traditional bots cannot handle well.
Options A, B, and D are incorrect or misleading:
* A is false because traditional automation isnotflexible across varied workflows.
* B is theoppositeof traditional automation - it's agentic.
* D is inaccurate because RPA handles repetitive, structured tasks very well - that's its strength.
By addressing C, UiPath bridges the gap between deterministic automation and intelligent, adaptive systems that can trulyscale across complex, real-world business scenarios.
37. Frage
What is the main purpose of using a context grounding strategy with an ECS Index in Agents designer canvas in Studio Web?
Antwort: D
Begründung:
Dis correct - the primary purpose of usingContext Grounding with an ECS (Enterprise Context Service) Indexin UiPath'sAgents designer canvasis to enablereal-time, dynamic retrieval of knowledgebased on the current user session or prompt inputs.
ECS indexes are built from documents, FAQs, policies, tickets, or any enterprise content and are used to:
* Provide agents withlive knowledge grounding
* Reduce hallucinations in LLM outputs
* Support tasks like Q&A, decision-making, and summarization
When a user inputs a query, the LLM canreference the ECS indexusing similarity search (usuallycosine similarity) to pullrelevant context chunksinto the prompt. This makes the agent smarter, safer, and more accurate.
Option A relates to deployment, not purpose.
B suggests hard-coded retrieval logic, which is the opposite of dynamic grounding.
C is about tuning, not the core purpose.
Context Grounding allows agents to actcontextually and intelligently, using up-to-date organizational data
- a foundational principle in UiPath's agentic architecture.
38. Frage
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